Datadog vs LogDNA

Overview

Datadog

Stacks9.8K

Followers8.2K

Votes861

LogDNA

Stacks97

Followers144

Votes18

Datadog vs LogDNA: What are the differences?

Introduction: This markdown presents a comparison between Datadog and LogDNA outlining their key differences.

Data Sources: Datadog primarily focuses on collecting metrics and logs from various sources like servers, databases, and containers. On the other hand, LogDNA specializes in collecting high-volume log data from multiple sources like servers, applications, and cloud services.
Visualization and Analysis: Datadog offers advanced visualization features and analytics tools that allow users to create custom dashboards, gain insights from data, and detect trends. In contrast, LogDNA emphasizes simple and efficient log search and filtering capabilities, making it easier for users to find specific log data quickly.
Integrations: Datadog provides a wide range of integrations with popular tools and services, enabling seamless connections and data exchange. LogDNA also offers integrations but focuses more on essential integrations with key platforms to streamline log collection and management processes.
Alerting and Monitoring: Datadog offers comprehensive alerting and monitoring capabilities, allowing users to set up custom alerts based on specific metrics and events. Conversely, LogDNA offers basic alerting features to notify users of critical log events but may not be as advanced as Datadog in this aspect.
Scalability: Datadog is known for its scalability, capable of handling large volumes of data and supporting enterprise-level operations. On the other hand, LogDNA is designed to scale efficiently and manage significant log data loads, making it suitable for growing businesses and organizations.
User Interface: Datadog provides a user-friendly and intuitive interface with extensive features and customization options, catering to the needs of technical and non-technical users alike. In contrast, LogDNA focuses on simplicity and ease of use, offering a clean and straightforward interface that prioritizes quick access to log data and search functionality.

In Summary, the key differences between Datadog and LogDNA lie in their primary focus on data sources, visualization capabilities, integrations, alerting and monitoring features, scalability, and user interface design.

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Advice on Datadog, LogDNA

Farzeem Diamond

Software Engineer at IVP

Jul 21, 2020

Needs adviceon

Datadog

Dynatrace

AppDynamics

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

1.59M views1.59M

Comments

Medeti

Jun 27, 2020

Needs adviceon

Amazon EKS

Kubernetes

AWS Elastic Load Balancing (ELB)

We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

1.51M views1.51M

Comments

Benoit

Principal Engineer at Sqreen

Sep 17, 2019

Decided

I chose Datadog APM because the much better APM insights it provides (flamegraph, percentiles by default).

The drawbacks of this decision are we had to move our production monitoring to TimescaleDB + Telegraf instead of NR Insight

NewRelic is definitely easier when starting out. Agent is only a lib and doesn't require a daemon

457k views457k

Comments

Detailed Comparison

Datadog	LogDNA
Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!	The easiest log management system you will ever use! LogDNA is a cloud-based log management system that allows engineering and devops to aggregate all system and application logs into one efficient platform. Save, store, tail and search app
14-day Free Trial for an unlimited number of hosts;200+ turn-key integrations for data aggregation;Clean graphs of StatsD and other integrations;Slice and dice graphs and alerts by tags, roles, and more;Easy-to-use search for hosts, metrics, and tags;Alert notifications via e-mail and PagerDuty;Receive alerts on any metric, for a single host or an entire cluster;Full API access in more than 15 languages;Overlay metrics and events across disparate sources;Out-of-the-box and customizable monitoring dashboards;Easy way to compute rates, ratios, averages, or integrals;Sampling intervals of 10 seconds;Mute all alerts with 1 click during upgrades and maintenance;Tools for team collaboration	Aggregate Logs & Analyze Related Events;Easy Setup in Minutes;Powerful Search & Alerts;Save what you see as a View;Modern User Interface;Tail -f Like a Boss;Debug & Troubleshoot Faster
Statistics
Stacks 9.8K	Stacks 97
Followers 8.2K	Followers 144
Votes 861	Votes 18
Pros & Cons
Pros 140 Monitoring for many apps (databases, web servers, etc) 107 Easy setup 87 Powerful ui 84 Powerful integrations 70 Great value Cons 20 Expensive 4 No errors exception tracking 2 External Network Goes Down You Wont Be Logging 1 Complicated	Pros 6 Easy setup 4 Cheap 3 Extremely fast 2 Powerful filtering and alerting functionality 1 Export data to S3 Cons 1 Limited visualization capabilities 1 Cannot copy & paste text from visualization
Integrations
NGINX Google App Engine Apache HTTP Server Java Docker Pingdom MySQL Ruby Python Memcached	No integrations available

What are some alternatives to Datadog, LogDNA?

New Relic

The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too.

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Raygun

Raygun gives you a window into how users are really experiencing your software applications. Detect, diagnose and resolve issues that are affecting end users with greater speed and accuracy.

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

AppSignal

AppSignal gives you and your team alerts and detailed metrics about your Ruby, Node.js or Elixir application. Sensible pricing, no aggressive sales & support by developers.

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

AppDynamics

AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.

Related Comparisons

Datadog vs LogDNA: What are the differences?

Introduction: This markdown presents a comparison between Datadog and LogDNA outlining their key differences.

Data Sources: Datadog primarily focuses on collecting metrics and logs from various sources like servers, databases, and containers. On the other hand, LogDNA specializes in collecting high-volume log data from multiple sources like servers, applications, and cloud services.
Visualization and Analysis: Datadog offers advanced visualization features and analytics tools that allow users to create custom dashboards, gain insights from data, and detect trends. In contrast, LogDNA emphasizes simple and efficient log search and filtering capabilities, making it easier for users to find specific log data quickly.
Integrations: Datadog provides a wide range of integrations with popular tools and services, enabling seamless connections and data exchange. LogDNA also offers integrations but focuses more on essential integrations with key platforms to streamline log collection and management processes.
Alerting and Monitoring: Datadog offers comprehensive alerting and monitoring capabilities, allowing users to set up custom alerts based on specific metrics and events. Conversely, LogDNA offers basic alerting features to notify users of critical log events but may not be as advanced as Datadog in this aspect.
Scalability: Datadog is known for its scalability, capable of handling large volumes of data and supporting enterprise-level operations. On the other hand, LogDNA is designed to scale efficiently and manage significant log data loads, making it suitable for growing businesses and organizations.
User Interface: Datadog provides a user-friendly and intuitive interface with extensive features and customization options, catering to the needs of technical and non-technical users alike. In contrast, LogDNA focuses on simplicity and ease of use, offering a clean and straightforward interface that prioritizes quick access to log data and search functionality.

Datadog vs LogDNA

Overview